Inverter-based Energy Resources Research Articles

Nonlinear Impact of Topological Configuration of Coupled Inverter-Based Resources on Interaction Harmonics Levels of Power Flow

The increasing level of harmonics in the power grid, driven by a substantial presence of coupled inverter-based energy resources (IBRs), poses a new challenge to power grid transient stability. This paper presents the findings from experiments and analytical studies on the impact of the topological configuration of coupled IBRs on the level of power flow harmonics in a distribution grid: (i) our findings report that the impact of grid topology on harmonics is nonlinear, which is in contrast to the common perception that the power grid operates as a large linear low-pass filter for harmonics; (ii) importantly, this study highlights that the influence of the topological configuration of inverters on the reduction of system-level harmonics is more substantial than the effect of line impedance, emphasizing the significance of grid topological configuration; (iii) furthermore, the observed reduction in harmonics is attributed to a harmonic cancellation effect achieved through self-compensation by all the coupled inverters without affecting the active power flow in the power grid. These findings propose a new approach to limit the penetration of complex IBR harmonics in the power grid from a system-wide perspective. This approach significantly differs from the component-level or localized solutions used today, such as inverter control, power filtering, and transformer tap changes.

Cyberattack on Phase-Locked Loops in Inverter-Based Energy Resources

This article reveals power grid vulnerabilities by manipulating the phase-locked loop (PLL) in an inverter-based energy resource (IBER). An attacker can exploit the off-nominal frequency operation of a power grid coupled with stability and tracking properties of an IBER PLL to manipulate the inverter and grid operations during strong and weak grid conditions. Employing a modified IEEE-14 bus system, simulation studies show that PLL-based attacks can cause: (i) revenue losses to IBER operators in weak and strong grid conditions, (ii) voltage limit violations and voltage profile deterioration at IBER bus under weak grid condition, and (iii) system-wide power-angle instability under weak and strong grid conditions. To detect the PLL-based attack, we propose attack severity index (ASI) by generating a detection error signal from local grid voltage measurements and PLL estimation. The attack is mitigated by incorporating security as an integral part of the PLL design, yielding a security-aware PLL.

Robust PLL-Based Grid Synchronization and Frequency Monitoring

Nowadays, the penetration of inverter-based energy resources is continuously increasing in low-voltage distribution grids. Their applications cover traditional renewable energy production and energy storage but also new applications such as charging points for electric vehicles, heat pumps, electrolyzers, etc. The power ratings range from a couple of kW to hundreds of kW. Utilities have, in the last few years, reported more challenges regarding power quality in distribution grids, e.g., high harmonic content, high unbalances, large voltage and frequency excursions, etc. Phase-Lock-Loop (PLL) algorithms are typically used for grid synchronization and decoupled control of power converters connected to the grid. Most of the research within PLLs is mainly focusing on grid voltage angle estimation while the byproducts of the algorithms, e.g., frequency and voltage magnitude, are often overlooked. However, both frequency and voltage magnitude estimations are crucial for grid code compliance. Practical considerations for implementation on microcontroller boards of these algorithms are also missing in most of the cases. The present paper proposes a modified PLL algorithm based on a Synchronous Reference Frame that is suitable for both grid synchronization and frequency monitoring, i.e., the estimation of RMS phase voltages and frequencies in highly distorted distribution grids. It also provides the tuning methodology and practical considerations for implementation on commercial DSP boards. The performance of the proposed approach is assessed through simulation studies and laboratory tests under a wide range of operational conditions, showing that the proposed PLL can estimate the grid frequency, for all considered grid events, with an accuracy of less than ±5 mHz, which is a significant improvement on the current state-of-the-art solutions, having an accuracy of at least ±20 mHz or more.

Transient Stability Improvement of Large-Scale Photovoltaic Grid Using a Flywheel as a Synchronous Machine

The global climate protection policy aimed at achieving a zero greenhouse gas emissions target has led to the fast incorporation of large-scale photovoltaics into the power network. The conventional AC grid was not modeled to be incorporated with large-scale non-synchronous inverter-based energy resources (IBR). Incorporating inertia-free IBR into the grid leads to technical issues such as the degradation of system strength and inertia, therefore affecting the safety and reliability of the electrical power system. This research introduced a new solution to incorporate a flywheel in the rotor of a synchronous machine to improve the dynamic inertia control during a system disruption and to maintain the constancy of the system. The objective of this work is to enhance large-scale photovoltaic systems in such a way that they can avoid failures during a fault. A model of transient constancy with two synchronous generators and a LSPV is established in PowerWorld modeling software. A line-to-ground and three-phase fault are simulated in a system with up to 50% IBR penetration. The outcomes showed that the power network was able to ride through faults (RTFs) and that the stability of frequency and voltage are enhanced because of a flywheel that improved grid inertia and strength.

Renewable Energy on the Apple Isle: Managing a high penetration of inverter-based energy resources in Tasmania

Tasmania is the island state of australia, located approximately 42° south of the equator off the southern mainland coast. Although Tasmania has abundant hydrogeneration capacity, it has recently achieved an instantaneous penetration of inverter-based resources (IBRs), exceeding 90%, in a system with a typical demand of around 1,200 MW.

Data-driven inverter-based Volt/VAr control for partially observable distribution networks

For active distribution networks (ADNs) integrated with massive inverter-based energy resources, it is impractical to maintain the accurate model and deploy measurements at all nodes due to the large-scale of ADNs. Thus, current models of ADNs are usually involving significant errors or even unknown. Moreover, ADNs are usually partially observable since only a few measurements are available at pilot nodes or nodes with significant users. To provide a practical Volt/Var control (VVC) strategy for such networks, a data-driven VVC method is proposed in this paper. Firstly, the system response policy, approximating the relationship between the control variables and states of monitoring nodes, is estimated by a recursive regression closed-form solution. Then, based on real-time measurements and the newly updated system response policy, a VVC strategy with convergence guarantee is realized. Since the recursive regression solution is embedded in the control stage, a data-driven closed-loop VVC framework is established. The effectiveness of the proposed method is validated in an unbalanced distribution system considering nonlinear loads where not only the rapid and self-adaptive voltage regulation is realized but also system-wide optimization is achieved.

Two-Stage Deep Reinforcement Learning for Inverter-Based Volt-VAR Control in Active Distribution Networks

Model-based Vol/VAR optimization method is widely used to eliminate voltage violations and reduce network losses. However, the parameters of active distribution networks(ADNs) are not onsite identified, so significant errors may be involved in the model and make the model-based method infeasible. To cope with this critical issue, we propose a novel two-stage deep reinforcement learning (DRL) method to improve the voltage profile by regulating inverter-based energy resources, which consists of offline stage and online stage. In the offline stage, a highly efficient adversarial reinforcement learning algorithm is developed to train an offline agent robust to the model mismatch. In the sequential online stage, we transfer the offline agent safely as the online agent to perform continuous learning and controlling online with significantly improved safety and efficiency. Numerical simulations on IEEE test networks not only demonstrate that the proposed adversarial reinforcement learning algorithm outperforms the state-of-art algorithm, but also show that our proposed two-stage method achieves much better performance than the existing DRL based methods in the online application.

Model-Free Voltage Control for Inverter-Based Energy Resources: Algorithm, Simulation and Field Test Verification

Dynamic voltage support is a type of critical ancillary service in electric power networks. With the increasing penetration of inverter-based renewable energy resources, utilizing their idle capacity to provide dynamic voltage support is becoming essential. However, the performance of traditional control methods is ofter limited due to the frequently changing operating point and network parameters, which are tuned only in a specific condition and suffer performance deterioration in others. This article presents a model-free dynamic voltage control method for inverter-based energy resources to supply dynamic voltage support. This method can dynamically estimate the system with pseudo gradients, in which a continuous parameter function is proposed to adaptively approximate the optimal intermediate parameters. It guarantees the performance when the host system operating states vary without tuning the controller's parameters, while in comparison the PID controllers require the parameters being tuned repeatedly. Both numerical simulation and field tests demonstrate that the proposed method significantly outperforms the classic PI and adaptive PI controllers, especially in the case when the host system varies over time.